Emotional support conversation aims at reducing the emotional distress of the help-seeker, which is a new and challenging task. It requires the system to explore the cause of help-seeker's emotional distress and understand their psychological intention to provide supportive responses. However, existing methods mainly focus on the sequential contextual information, ignoring the hierarchical relationships with the global cause and local psychological intention behind conversations, thus leads to a weak ability of emotional support. In this paper, we propose a Global-to-Local Hierarchical Graph Network to capture the multi-source information (global cause, local intentions and dialog history) and model hierarchical relationships between them, which consists of a multi-source encoder, a hierarchical graph reasoner, and a global-guide decoder. Furthermore, a novel training objective is designed to monitor semantic information of the global cause. Experimental results on the emotional support conversation dataset, ESConv, confirm that the proposed GLHG has achieved the state-of-the-art performance on the automatic and human evaluations. The code will be released in here \footnote{\small{~https://github.com/pengwei-iie/GLHG}}.
翻译:情感支持对话旨在减少寻求帮助者的情感痛苦,这是一项具有挑战性的新任务,要求系统探索寻求帮助者情感痛苦的原因,并理解其提供支持性反应的心理意图;然而,现有方法主要侧重于顺序背景信息,忽视与全球原因的等级关系以及对话背后的当地心理意图,从而导致情感支持能力薄弱。在本文件中,我们提议建立一个全球到地方的等级图网络,以捕捉多种来源信息(全球原因、地方意图和对话历史)以及他们之间的模式等级关系,包括多源编码器、一个等级图表解释器和全球指南解码器。此外,还设计了一个新的培训目标,以监测全球原因的语义信息。关于情感支持对话数据集的实验结果,ESConv,确认拟议的GLHG在自动和人文评估方面实现了最先进的表现。该代码将发布在这里\ footoot \ smlicks://github.com/pengweiiiie/GHHGHQ。